{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db6c71b7-81aa-4e42-9220-503df800b6ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 用geopandas读取shapefile\n",
    "# 用geopandas可视化shapefile\n",
    "# 应shapely创建新的空间要素：点，线，面\n",
    "# 用geopandas可视化新建的空间要素\n",
    "# 利用geopandas输出shapefile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f61a18b2-1583-4753-a66f-46c93553672d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T12:54:07.323029Z",
     "iopub.status.busy": "2021-06-08T12:54:07.322742Z",
     "iopub.status.idle": "2021-06-08T12:54:07.818668Z",
     "shell.execute_reply": "2021-06-08T12:54:07.818018Z",
     "shell.execute_reply.started": "2021-06-08T12:54:07.322966Z"
    }
   },
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "70454a16-c357-4bc2-90d6-7b61c32c8a29",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T12:56:01.042865Z",
     "iopub.status.busy": "2021-06-08T12:56:01.042541Z",
     "iopub.status.idle": "2021-06-08T12:56:01.084855Z",
     "shell.execute_reply": "2021-06-08T12:56:01.084133Z",
     "shell.execute_reply.started": "2021-06-08T12:56:01.042836Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>centroid_x</th>\n",
       "      <th>centroid_y</th>\n",
       "      <th>qh</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>114.143157</td>\n",
       "      <td>22.577605</td>\n",
       "      <td>罗湖</td>\n",
       "      <td>POLYGON ((114.10006 22.53431, 114.09969 22.535...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>114.041535</td>\n",
       "      <td>22.546180</td>\n",
       "      <td>福田</td>\n",
       "      <td>POLYGON ((113.98578 22.51348, 113.98558 22.523...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>114.270206</td>\n",
       "      <td>22.596432</td>\n",
       "      <td>盐田</td>\n",
       "      <td>POLYGON ((114.22772 22.54290, 114.22643 22.543...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>113.851387</td>\n",
       "      <td>22.679120</td>\n",
       "      <td>宝安</td>\n",
       "      <td>MULTIPOLYGON (((113.81831 22.54676, 113.81816 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>113.926290</td>\n",
       "      <td>22.766157</td>\n",
       "      <td>光明</td>\n",
       "      <td>POLYGON ((113.98587 22.80304, 113.98605 22.802...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   centroid_x  centroid_y  qh  \\\n",
       "0  114.143157   22.577605  罗湖   \n",
       "1  114.041535   22.546180  福田   \n",
       "2  114.270206   22.596432  盐田   \n",
       "3  113.851387   22.679120  宝安   \n",
       "4  113.926290   22.766157  光明   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((114.10006 22.53431, 114.09969 22.535...  \n",
       "1  POLYGON ((113.98578 22.51348, 113.98558 22.523...  \n",
       "2  POLYGON ((114.22772 22.54290, 114.22643 22.543...  \n",
       "3  MULTIPOLYGON (((113.81831 22.54676, 113.81816 ...  \n",
       "4  POLYGON ((113.98587 22.80304, 113.98605 22.802...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = gpd.GeoDataFrame.from_file(r'../data/Shapefiles/sz.shp')\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "075b49f6-7db2-49a9-97ea-941cac501ebe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T12:57:03.822759Z",
     "iopub.status.busy": "2021-06-08T12:57:03.822499Z",
     "iopub.status.idle": "2021-06-08T12:57:03.972899Z",
     "shell.execute_reply": "2021-06-08T12:57:03.972195Z",
     "shell.execute_reply.started": "2021-06-08T12:57:03.822734Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "834e04d9-55df-4735-bd08-1efd46c207b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T12:58:29.653803Z",
     "iopub.status.busy": "2021-06-08T12:58:29.653573Z",
     "iopub.status.idle": "2021-06-08T12:58:29.662455Z",
     "shell.execute_reply": "2021-06-08T12:58:29.661309Z",
     "shell.execute_reply.started": "2021-06-08T12:58:29.653779Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
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      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x125faaa60>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "94941b5c-bab9-45e3-b09d-cfd0e1473801",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T13:00:03.024005Z",
     "iopub.status.busy": "2021-06-08T13:00:03.023741Z",
     "iopub.status.idle": "2021-06-08T13:00:08.855513Z",
     "shell.execute_reply": "2021-06-08T13:00:08.854731Z",
     "shell.execute_reply.started": "2021-06-08T13:00:03.023963Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2 = gpd.GeoDataFrame.from_file(r'../data/Shapefiles/shenzhen_osmroad.shp')\n",
    "data2.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e2c903da-fc59-497c-89f7-bdc219319734",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T13:03:28.336748Z",
     "iopub.status.busy": "2021-06-08T13:03:28.336501Z",
     "iopub.status.idle": "2021-06-08T13:03:28.339968Z",
     "shell.execute_reply": "2021-06-08T13:03:28.339193Z",
     "shell.execute_reply.started": "2021-06-08T13:03:28.336722Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "lat1 = 22.447837\n",
    "lng1 = 113.75194\n",
    "lat2 = 22.864748\n",
    "lng2 = 114.624187"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "50b9b277-3c20-4ff7-952c-e88478d35a6a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T13:04:48.976418Z",
     "iopub.status.busy": "2021-06-08T13:04:48.976066Z",
     "iopub.status.idle": "2021-06-08T13:04:48.980308Z",
     "shell.execute_reply": "2021-06-08T13:04:48.979303Z",
     "shell.execute_reply.started": "2021-06-08T13:04:48.976387Z"
    }
   },
   "outputs": [],
   "source": [
    "from shapely.geometry import Point\n",
    "p1=Point(lng1,lat1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6dccd174-597b-4246-b6b4-32aadd2cd27d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T13:05:32.314195Z",
     "iopub.status.busy": "2021-06-08T13:05:32.313928Z",
     "iopub.status.idle": "2021-06-08T13:05:32.317698Z",
     "shell.execute_reply": "2021-06-08T13:05:32.316659Z",
     "shell.execute_reply.started": "2021-06-08T13:05:32.314168Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "p2=Point(lng2,lat2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "02f4e9d4-b60b-480b-a852-a006b996c910",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T13:07:51.725857Z",
     "iopub.status.busy": "2021-06-08T13:07:51.725622Z",
     "iopub.status.idle": "2021-06-08T13:07:51.732818Z",
     "shell.execute_reply": "2021-06-08T13:07:51.731697Z",
     "shell.execute_reply.started": "2021-06-08T13:07:51.725833Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "geo = gpd.GeoDataFrame(columns=[\"pid\", \"geometry\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1f650802-5950-45eb-89b6-74bc9a4b87db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T13:09:21.730166Z",
     "iopub.status.busy": "2021-06-08T13:09:21.729928Z",
     "iopub.status.idle": "2021-06-08T13:09:21.783380Z",
     "shell.execute_reply": "2021-06-08T13:09:21.782672Z",
     "shell.execute_reply.started": "2021-06-08T13:09:21.730142Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mgeo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify_integrity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;34m'DataFrame'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Append rows of `other` to the end of caller, returning a new object.\n",
       "\n",
       "Columns in `other` that are not in the caller are added as new columns.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "other : DataFrame or Series/dict-like object, or list of these\n",
       "    The data to append.\n",
       "ignore_index : bool, default False\n",
       "    If True, the resulting axis will be labeled 0, 1, …, n - 1.\n",
       "verify_integrity : bool, default False\n",
       "    If True, raise ValueError on creating index with duplicates.\n",
       "sort : bool, default False\n",
       "    Sort columns if the columns of `self` and `other` are not aligned.\n",
       "\n",
       "    .. versionchanged:: 1.0.0\n",
       "\n",
       "        Changed to not sort by default.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "DataFrame\n",
       "\n",
       "See Also\n",
       "--------\n",
       "concat : General function to concatenate DataFrame or Series objects.\n",
       "\n",
       "Notes\n",
       "-----\n",
       "If a list of dict/series is passed and the keys are all contained in\n",
       "the DataFrame's index, the order of the columns in the resulting\n",
       "DataFrame will be unchanged.\n",
       "\n",
       "Iteratively appending rows to a DataFrame can be more computationally\n",
       "intensive than a single concatenate. A better solution is to append\n",
       "those rows to a list and then concatenate the list with the original\n",
       "DataFrame all at once.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))\n",
       ">>> df\n",
       "   A  B\n",
       "0  1  2\n",
       "1  3  4\n",
       ">>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'))\n",
       ">>> df.append(df2)\n",
       "   A  B\n",
       "0  1  2\n",
       "1  3  4\n",
       "0  5  6\n",
       "1  7  8\n",
       "\n",
       "With `ignore_index` set to True:\n",
       "\n",
       ">>> df.append(df2, ignore_index=True)\n",
       "   A  B\n",
       "0  1  2\n",
       "1  3  4\n",
       "2  5  6\n",
       "3  7  8\n",
       "\n",
       "The following, while not recommended methods for generating DataFrames,\n",
       "show two ways to generate a DataFrame from multiple data sources.\n",
       "\n",
       "Less efficient:\n",
       "\n",
       ">>> df = pd.DataFrame(columns=['A'])\n",
       ">>> for i in range(5):\n",
       "...     df = df.append({'A': i}, ignore_index=True)\n",
       ">>> df\n",
       "   A\n",
       "0  0\n",
       "1  1\n",
       "2  2\n",
       "3  3\n",
       "4  4\n",
       "\n",
       "More efficient:\n",
       "\n",
       ">>> pd.concat([pd.DataFrame([i], columns=['A']) for i in range(5)],\n",
       "...           ignore_index=True)\n",
       "   A\n",
       "0  0\n",
       "1  1\n",
       "2  2\n",
       "3  3\n",
       "4  4\n",
       "\u001b[0;31mFile:\u001b[0m      /usr/local/lib/python3.9/site-packages/pandas/core/frame.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?geo.append"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b16e7ba0-a2e5-4fe7-8726-31c29dd2ba4f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T13:11:31.722768Z",
     "iopub.status.busy": "2021-06-08T13:11:31.722416Z",
     "iopub.status.idle": "2021-06-08T13:11:31.730221Z",
     "shell.execute_reply": "2021-06-08T13:11:31.729279Z",
     "shell.execute_reply.started": "2021-06-08T13:11:31.722722Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "geo = geo.append({\"pid\":1,\"geometry\":p1}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ccc458cb-7319-45af-bea1-1d4a39b67313",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T13:11:39.096029Z",
     "iopub.status.busy": "2021-06-08T13:11:39.095696Z",
     "iopub.status.idle": "2021-06-08T13:11:39.103399Z",
     "shell.execute_reply": "2021-06-08T13:11:39.102473Z",
     "shell.execute_reply.started": "2021-06-08T13:11:39.095994Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "geo = geo.append({\"pid\":2,\"geometry\":p2}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c72cd53f-d5f8-4994-bb7c-6c70ca34272e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T13:11:56.722600Z",
     "iopub.status.busy": "2021-06-08T13:11:56.722230Z",
     "iopub.status.idle": "2021-06-08T13:11:56.860279Z",
     "shell.execute_reply": "2021-06-08T13:11:56.859659Z",
     "shell.execute_reply.started": "2021-06-08T13:11:56.722571Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "geo.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "388c907d-4d5e-42a3-bbed-b2eb36a6ec03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T13:12:44.714474Z",
     "iopub.status.busy": "2021-06-08T13:12:44.714241Z",
     "iopub.status.idle": "2021-06-08T13:12:44.718021Z",
     "shell.execute_reply": "2021-06-08T13:12:44.717019Z",
     "shell.execute_reply.started": "2021-06-08T13:12:44.714451Z"
    }
   },
   "outputs": [],
   "source": [
    "a= [[lng1,lat1],[lng2,lat2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c360e558-9134-474e-a54b-d9fc4343935a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T13:13:12.770946Z",
     "iopub.status.busy": "2021-06-08T13:13:12.770660Z",
     "iopub.status.idle": "2021-06-08T13:13:12.775145Z",
     "shell.execute_reply": "2021-06-08T13:13:12.774049Z",
     "shell.execute_reply.started": "2021-06-08T13:13:12.770920Z"
    }
   },
   "outputs": [],
   "source": [
    "nd = pd.DataFrame(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8aaa0c44-7b91-46e7-9c61-995681633783",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T13:13:34.702763Z",
     "iopub.status.busy": "2021-06-08T13:13:34.702444Z",
     "iopub.status.idle": "2021-06-08T13:13:34.707084Z",
     "shell.execute_reply": "2021-06-08T13:13:34.705957Z",
     "shell.execute_reply.started": "2021-06-08T13:13:34.702708Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "nd.columns=[\"lng\",\"lat\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6b2ae690-b451-42f9-b6e9-6dc3fe52bbf8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T13:16:14.481544Z",
     "iopub.status.busy": "2021-06-08T13:16:14.481290Z",
     "iopub.status.idle": "2021-06-08T13:16:14.488360Z",
     "shell.execute_reply": "2021-06-08T13:16:14.487380Z",
     "shell.execute_reply.started": "2021-06-08T13:16:14.481519Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "nd[\"geometry\"]=nd.apply(lambda r:Point(r['lng'],r['lat']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "282c4010-2adf-4390-8873-dabf1ba61bf1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T13:17:15.841897Z",
     "iopub.status.busy": "2021-06-08T13:17:15.841567Z",
     "iopub.status.idle": "2021-06-08T13:17:15.846750Z",
     "shell.execute_reply": "2021-06-08T13:17:15.845667Z",
     "shell.execute_reply.started": "2021-06-08T13:17:15.841867Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "nd = gpd.GeoDataFrame(nd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "86791be3-eeaa-486e-a0b4-392bddc8188f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T13:17:21.002296Z",
     "iopub.status.busy": "2021-06-08T13:17:21.001956Z",
     "iopub.status.idle": "2021-06-08T13:17:21.142092Z",
     "shell.execute_reply": "2021-06-08T13:17:21.141481Z",
     "shell.execute_reply.started": "2021-06-08T13:17:21.002263Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAC/CAYAAADw8hcjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAN90lEQVR4nO3cf5Cd1V3H8fdHkrYpIOmUnTpJsGGspkakpK4tldpOwRqaYZBqO9YiI9KKVbRQMWjEUfzDsRoHx8pYxNLiH7F2LAk609CQP5ipzJR0lhAIEJLB/iRBu3UagZKOhH794950brd3d+9m7+5NDu/XzM4+e55zzv0+Z+589tlz791UFZKkdv3AqAuQJC0sg16SGmfQS1LjDHpJapxBL0mNM+glqXFLRl1AP2eeeWatXr161GVI0knjgQce+EZVjfU7d0IG/erVq5mYmBh1GZJ00kjylenOuXUjSY0z6CWpcSfk1o0kvZjc9eBBNu/Yz6HDR1ixfBkb16/hsnUrhza/QS9JI3TXgwfZtHUvR55/AYCDh4+waetegKGFvVs3kjRCm3fs/27IH3Pk+RfYvGP/0B7DoJekETp0+Mic2o+HQS9JI7Ri+bI5tR8Pg16SRmjj+jUsW3rK97QtW3oKG9evGdpj+GKsJI3QsRdcfdeNJDXssnUrhxrsU7l1I0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaN2vQJzkryb1JHkvyaJJru+2bkzye5OEk25Isn2b8h7rjHknyySQvG/I1SJJmMMgd/VHg+qpaC5wPXJNkLbATOKeqzgUOAJumDkyyEvggMF5V5wCnAO8ZVvGSpNnNGvRV9VRV7e4ePwPsA1ZW1T1VdbTb7X5g1TRTLAGWJVkCvBw4NP+yJUmDmtMefZLVwDpg15RTVwF3T+1fVQeBvwa+CjwF/G9V3XNclUqSjsvAQZ/kNOBO4Lqqerqn/UY62ztb+ox5BfALwNnACuDUJL86zfxXJ5lIMjE5OTm3q5AkTWugoE+ylE7Ib6mqrT3tVwKXAJdXVfUZ+nPAl6pqsqqeB7YCP9PvMarqtqoar6rxsbGxOV6GJGk6g7zrJsDtwL6qurmn/WLgBuDSqnpumuFfBc5P8vLuPBfR2eOXJC2SQe7oLwCuAC5Msqf7tQG4BTgd2NltuxUgyYok2wGqahfwaWA3sLf7eLctwHVIkqaR/jsuozU+Pl4TExOjLkOSThpJHqiq8X7n/GSsJDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxs0a9EnOSnJvkseSPJrk2m775iSPJ3k4ybYky/uMXZNkT8/X00muG/5lSJKmM8gd/VHg+qpaC5wPXJNkLbATOKeqzgUOAJumDqyq/VV1XlWdB/wU8BywbVjFS5JmN2vQV9VTVbW7e/wMsA9YWVX3VNXRbrf7gVWzTHUR8J9V9ZX5FCxJmps57dEnWQ2sA3ZNOXUVcPcsw98DfHIujydJmr+Bgz7JacCdwHVV9XRP+410tne2zDD2JcClwL/O0OfqJBNJJiYnJwctS5I0i4GCPslSOiG/paq29rRfCVwCXF5VNcMU7wB2V9V/T9ehqm6rqvGqGh8bGxuoeEnS7JbM1iFJgNuBfVV1c0/7xcANwFur6rlZpvkV3LaRpJEY5I7+AuAK4MKet0luAG4BTgd2dttuBUiyIsn2Y4OTnAq8HdjaZ25J0gKb9Y6+qu4D0ufU9j5tVNUhYEPPz98CXnm8BUqS5sdPxkpS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWrcrEGf5Kwk9yZ5LMmjSa7ttm9O8niSh5NsS7J8mvHLk3y623dfkjcN+RokSTMY5I7+KHB9Va0FzgeuSbIW2AmcU1XnAgeATdOM/1vgs1X1WuB1wL75ly1JGtSsQV9VT1XV7u7xM3SCemVV3VNVR7vd7gdWTR2b5AzgLcDt3fH/V1WHh1S7JGkAc9qjT7IaWAfsmnLqKuDuPkPOBiaBTyR5MMnHkpw6zdxXJ5lIMjE5OTmXsiRJMxg46JOcBtwJXFdVT/e030hne2dLn2FLgNcDH62qdcC3gD/sN39V3VZV41U1PjY2NodLkCTNZKCgT7KUTshvqaqtPe1XApcAl1dV9Rn6JPBkVR37C+DTdIJfkrRIBnnXTejsse+rqpt72i8GbgAurarn+o2tqv8CvpZkTbfpIuCxeVctSRrYkgH6XABcAexNsqfb9kfAR4CXAjs7vwu4v6o+kGQF8LGq2tDt+7vAliQvAb4I/PoQ65ckzWLWoK+q+4D0ObV9mv6HgA09P+8Bxo+zPknSPPnJWElqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY2bNeiTnJXk3iSPJXk0ybXd9s1JHk/ycJJtSZZPM/7LSfYm2ZNkYsj1S5JmMcgd/VHg+qpaC5wPXJNkLbATOKeqzgUOAJtmmONtVXVeVY3Pu2JJ0pzMGvRV9VRV7e4ePwPsA1ZW1T1VdbTb7X5g1cKVKUk6XnPao0+yGlgH7Jpy6irg7mmGFXBPkgeSXD3D3FcnmUgyMTk5OZeyJEkzGDjok5wG3AlcV1VP97TfSGd7Z8s0Q99cVa8H3kFn2+ct/TpV1W1VNV5V42NjYwNfgCRpZgMFfZKldEJ+S1Vt7Wm/ErgEuLyqqt/YqjrY/f51YBvwhnnWLEmag0HedRPgdmBfVd3c034xcANwaVU9N83YU5OcfuwY+HngkWEULkkazCB39BcAVwAXdt8iuSfJBuAW4HRgZ7ftVoAkK5Js7459FXBfkoeALwCfqarPDv8yJEnTWTJbh6q6D0ifU9v7tFFVh4AN3eMvAq+bT4GSpPnxk7GS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGjfrv0A4Wdz14EE279jPocNHWLF8GRvXr+GydStHXZYkjVwTQX/XgwfZtHUvR55/AYCDh4+waeteAMNe0oteE1s3m3fs/27IH3Pk+RfYvGP/iCqSpBNHE0F/6PCRObVL0otJE0G/YvmyObVL0otJE0G/cf0ali095Xvali09hY3r14yoIkk6cTTxYuyxF1x9140kfb8mgh46YW+wS9L3a2LrRpI0PYNekhqXqhp1Dd8nySTwlRGWcCbwjRE+/onINenPdenPdelvIdfl1VU11u/ECRn0o5ZkoqrGR13HicQ16c916c916W9U6+LWjSQ1zqCXpMYZ9P3dNuoCTkCuSX+uS3+uS38jWRf36CWpcd7RS1Ljmg76JB9P8vUkj/S0vTvJo0m+k2S8p/0NSfZ0vx5K8s5p5rwoye5uv/uSvGYxrmWY5rIuPed/OMmzSX5/mjnPTrIryRNJPpXkJQt5DQthgdZlS5L9SR7pzr90Ia9hISzEuvT0+0iSZxei7oW2QM+XJPnzJAeS7EvywWHU2nTQA3cAF09pewT4ReBzfdrHq+q87ph/SNLvX0R8FLi82++fgT8eYr2L5Q4GX5djbgbunmHOvwT+pqpeA3wTeN88axyFOxj+umwBXgv8JLAMeP/8ShyJOxj+utANwlfMt7gRuoPhr8uVwFnAa6vqx4F/mV+JHc38r5t+qupzSVZPadsHkGRq3+d6fnwZMN2LFwX8YPf4DODQMGpdTHNZl27bZcCXgG/1my+dQRcC7+02/RNwE51fiieNYa9Ld/z2nv5fAFYNp9rFsxDrkuQUYDOd50zfv55PdAuxLsBvAe+tqu905/v6MGpt/Y5+TpK8McmjwF7gA1V1tE+39wPbkzwJXAF8eDFrXGxJTgP+APizGbq9Ejjcs15PAk3/h7kB16W3/1I6z5fPLmRdozaHdfkd4N+r6qmFr2r05rAuPwL8cpKJJHcn+dFhPL5B36OqdlXVTwA/DWxK8rI+3T4EbKiqVcAn6Pwp1rKb6GzJnJT7qAvoJua2Ln8PfK6q/mPhSjoh3MQs65JkBfBu4O8Wq6gTwE0M9nx5KfDt7qdn/xH4+DAevOmtm+NVVfu6LxCdA0wca08yBryuqnZ1mz5F43dowBuBdyX5K2A58J0k366qW3r6/A+wPMmS7l39KuDg4pe6qAZZFwCS/CkwBvzm4pY4EoOsyzrgNcAT3S2Olyd5ovv6TqsGfb48CWztHm+jczM5bwZ9V5Kzga9V1dEkr6bzAtqXp3T7JnBGkh+rqgPA24F9i1vp4qqqnz12nOQm4NmpT86qqiT3Au+i8+LRrwH/tph1LrZB1qV77v3AeuCiY/uuLRvw+fIZ4Id6+j3beMgP/HwB7gLeRmcv/63AgWE8ftNbN0k+CXweWJPkySTvS/LO7v76m4DPJNnR7f5m4KEke+j8Jv3tqvpGd57tSVZ071Z/A7gzyUN09lw3LvJlzdsc12WmebZ3/wyHzv7j7yV5gs6e/e0LVf9CWaB1uRV4FfD5dN6S+ycLdgELZIHW5aS3QOvyYeCXkuwF/oIhvUvLT8ZKUuOavqOXJBn0ktQ8g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ17v8BX5lcvyO9O2IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nd.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e35bf640-707d-44fc-95ff-87d76eee38a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T13:18:10.536235Z",
     "iopub.status.busy": "2021-06-08T13:18:10.535840Z",
     "iopub.status.idle": "2021-06-08T13:18:10.540775Z",
     "shell.execute_reply": "2021-06-08T13:18:10.540023Z",
     "shell.execute_reply.started": "2021-06-08T13:18:10.536203Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "nnd = nd[['lng','lat']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "00550023-ffc5-442e-8b09-4d3cda334d89",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T13:19:53.615775Z",
     "iopub.status.busy": "2021-06-08T13:19:53.615541Z",
     "iopub.status.idle": "2021-06-08T13:19:53.626399Z",
     "shell.execute_reply": "2021-06-08T13:19:53.625242Z",
     "shell.execute_reply.started": "2021-06-08T13:19:53.615751Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lng</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>113.751940</td>\n",
       "      <td>22.447837</td>\n",
       "      <td>114.624187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>114.624187</td>\n",
       "      <td>22.864748</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          lng        lat        lng1\n",
       "0  113.751940  22.447837  114.624187\n",
       "1  114.624187  22.864748         NaN"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nnd[\"lng1\"]=nnd[\"lng\"].shift(-1)\n",
    "nnd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "04e16f38-84d8-4341-ad56-906cfe39c30f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T13:20:14.584431Z",
     "iopub.status.busy": "2021-06-08T13:20:14.584200Z",
     "iopub.status.idle": "2021-06-08T13:20:14.595054Z",
     "shell.execute_reply": "2021-06-08T13:20:14.594296Z",
     "shell.execute_reply.started": "2021-06-08T13:20:14.584408Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lng</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng1</th>\n",
       "      <th>lat1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>113.751940</td>\n",
       "      <td>22.447837</td>\n",
       "      <td>114.624187</td>\n",
       "      <td>22.864748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>114.624187</td>\n",
       "      <td>22.864748</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          lng        lat        lng1       lat1\n",
       "0  113.751940  22.447837  114.624187  22.864748\n",
       "1  114.624187  22.864748         NaN        NaN"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nnd[\"lat1\"]=nnd[\"lat\"].shift(-1)\n",
    "nnd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "84b07cf3-61ad-4559-a0b8-212aaac907b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T13:21:55.506812Z",
     "iopub.status.busy": "2021-06-08T13:21:55.506426Z",
     "iopub.status.idle": "2021-06-08T13:21:55.511815Z",
     "shell.execute_reply": "2021-06-08T13:21:55.511122Z",
     "shell.execute_reply.started": "2021-06-08T13:21:55.506781Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "nnd=nnd[-nnd['lng1'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ab7f59d9-4a11-4a7f-b065-bfa544934d3f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-08T13:25:49.803379Z",
     "iopub.status.busy": "2021-06-08T13:25:49.803114Z",
     "iopub.status.idle": "2021-06-08T13:25:49.838061Z",
     "shell.execute_reply": "2021-06-08T13:25:49.836040Z",
     "shell.execute_reply.started": "2021-06-08T13:25:49.803330Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'lng2'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3079\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3080\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'lng2'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-58-52a819c1af31>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mshapely\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeometry\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLineString\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mnnd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mLineString\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lng'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lat'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lng2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lat2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, axis, raw, result_type, args, **kwds)\u001b[0m\n\u001b[1;32m   7766\u001b[0m             \u001b[0mkwds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7767\u001b[0m         )\n\u001b[0;32m-> 7768\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   7769\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7770\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapplymap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_action\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    183\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_raw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_empty_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 276\u001b[0;31m         \u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_series_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m         \u001b[0;31m# wrap results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    288\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_gen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m                 \u001b[0;31m# ignore SettingWithCopy here in case the user mutates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 290\u001b[0;31m                 \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    291\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m                     \u001b[0;31m# If we have a view on v, we need to make a copy because\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-58-52a819c1af31>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(r)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mshapely\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeometry\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLineString\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mnnd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mLineString\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lng'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lat'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lng2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lat2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    851\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    852\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 853\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    854\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m    959\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    960\u001b[0m         \u001b[0;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 961\u001b[0;31m         \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    962\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    963\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3080\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3082\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3084\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'lng2'"
     ]
    }
   ],
   "source": [
    "from shapely.geometry import LineString\n",
    "nnd.apply(lambda r:LineString([[r['lng'],r['lat']],[r['lng2'],r['lat2']]]), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cf15a20-c0ec-48c8-a49b-6a2ee555681b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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